Fitting rainfall profiles

library(umbrella)

Below is an example workflow for using the umbrella package to fit rainfall profiles for use in malariasimulation.

First you’ll need to download the rasters and extract the raw data. There are some helper function to download global daily rainfall rasters in the package. You can use the get_urls() function to create the URLs to get daily rainfall rasters for a given year. After that the download_raster() can be used to download the rasters to your device. Beware that the set of unzipped rasters for a whole year will take up quite a bit of memory (each raster is about 2.2 MB).

Once you have you set of rasters you can load them and extract the data. For this I recommend getting a sf shape file with the boundaries of the area you would like to extract the data for. The R package terra is a great way to extract the data, something like:

my_raster_files <- c("rainfall1.tif", "rainfall2.tif")
my_raster <- terra::rast(my_raster_files)
my_sf <- load("my_sf.rds")
extract_data <- terra::extract(my_raster, my_sf, fun = mean)

Once you’ve extracted and tidied the output, you might have somethig like:

head(rainfall)
#>   day rainfall
#> 1   1     0.00
#> 2   2     0.00
#> 3   3     0.00
#> 4   4     0.28
#> 5   5     0.17
#> 6   6     0.03

Lets fit a simple seasonal profile to this dataset, and a profile with an imposed minimum rainfall floor.

fit1 <- fit_fourier(rainfall = rainfall$rainfall, t = rainfall$day, floor = 0)
predict_1 <- fourier_predict(coef = fit1$coefficients, t = 1:365, floor = 0)

fit2 <- fit_fourier(rainfall = rainfall$rainfall, t = rainfall$day, floor = 1)
predict_2 <- fourier_predict(coef = fit1$coefficients, t = 1:365, floor = 1)

plot(rainfall, xlab = "Day", ylab = "Rainfall", pch = 19)
lines(predict_1, col = "deeppink", lwd = 2)
lines(predict_2, col = "dodgerblue", lwd = 2, lty = 2)